Road markers provide vital information to ensure traffic safety. Different sets of markers are normally used between the highways and the normal road. At the normal road for example, the double lane markers are used to indicate the hazardous area, where overtaking is prohibited while broken marker lane indicate otherwise. To avoid traffic accidents and provide safety, these markers should be accurately detected and classified, which is best solved via vision detection approach. Marker type classification is however affected by the changing sun illumination throughout the day. In this paper, real-time recognition of these markers is developed using the artificial neural network (ANN) to alert the users while driving. The accuracy of the scheme is observed when different input features (geometrical and texture) and image pixels are fed for recognizing broken and double lane markers. A very high accuracy result with low error rate is obtained at 98.83% (10-fold cross validation) accuracy detection using additional features, compared with ~95% by using only the image pixels as the input vector and average processing time is at ~30ms per frame.
The robotic arm structure and control algorithm are designed for a purpose, to pick and place an object task at underwater which is attached to a ROV (Remotely Operated Underwater Vehicle). It is controlled by an innovated gesture control system, Leap Motion controller. The arm structure of pick and place is controlled by Arduino as microcontroller to control the angles and displacements of the servomotor precisely. The detection of position and orientation of the fingers and hands processed by develop control algorithm in Javascript language and sent to the Arduino. Meanwhile, a detailed 3D drawing is drawn precisely by using SolidWorks for the fabrication. After the platform is completed, kinematic and inverse kinematic equations and calculations are programed into JavaScript language for the control algorithm. Lastly, the hardware and software combined all together. With developed control algorithm, the robotic arm mimics human’s fingers and arm movements which more user friendly interface especially underwater scavenging and salvaging. Since it designed for underwater, the accuracy and precision are crucial for robotic arms, it undergo several experiments and tests for investigate reliability performance of developed robotic arm.
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